Fast and Efficient Root Phenotyping via Pose Estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Fast and efficient root phenotyping via pose estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Methyl jasmonate influences ethylene formation, defense systems, nutrient homeostasis and carbohydrate metabolism to alleviate arsenic-induced stress in rice (Oryza sativa)

The plant growth regulator, jasmonic acid (JA) has emerged as important molecule and involved in key processes of plants. In this study, we investigated the role of methyl jasmonate (MeJA) in achieving tolerance mechanisms against arsenic (As) stress in rice (Oryza sativa). Arsenic toxicity is a major global concern that significantly deteriorate rice production. The application of MeJA (20 μM) and ethylene (150 μL L-1) both individually and/or in combination were found significant in protecting against As-induced toxicity in rice, and significantly improved defense systems. The study shown that the positive influence of MeJA in promoting carbohydrate metabolism, photosynthesis and growth under As stress were the result of its interplay with ethylene biosynthesis and reduced oxidative stress-mediated cellular injuries and cell deaths. Interestingly, the use of JA biosynthesis inhibitor, neomycin (Neo) and ethylene biosynthesis inhibitor, aminoethoxyvinylglycine (AVG) overturned the effects of MeJA and ethylene on plant growth under As stress. From the pooled data, it may also be concluded that Neo treatment to MeJA- treated rice plants restricted JA-mediated responses, implying that application of MeJA modulated ethylene- dependent pathways in response to As stress. Thus, the action of MeJA in As tolerance is found to be mediated by ethylene. The study will shed light on the mechanisms that could be used to ensure the sustainability of rice plants under As stress.

Installation and imaging of thousands of minirhizotrons to phenotype root systems of field-grown plants

BACKGROUND

Roots are vital to plant performance because they acquire resources from the soil and provide anchorage. However, it remains difficult to assess root system size and distribution because roots are inaccessible in the soil. Existing methods to phenotype entire root systems range from slow, often destructive, methods applied to relatively small numbers of plants in the field to rapid methods that can be applied to large numbers of plants in controlled environment conditions. Much has been learned recently by extensive sampling of the root crown portion of field-grown plants. But, information on large-scale genetic and environmental variation in the size and distribution of root systems in the field remains a key knowledge gap. Minirhizotrons are the only established, non-destructive technology that can address this need in a standard field trial. Prior experiments have used only modest numbers of minirhizotrons, which has limited testing to small numbers of genotypes or environmental conditions. This study addressed the need for methods to install and collect images from thousands of minirhizotrons and thereby help break the phenotyping bottleneck in the field.

RESULTS

Over three growing seasons, methods were developed and refined to install and collect images from up to 3038 minirhizotrons per experiment. Modifications were made to four tractors and hydraulic soil corers mounted to them. High quality installation was achieved at an average rate of up to 84.4 minirhizotron tubes per tractor per day. A set of four commercially available minirhizotron camera systems were each transported by wheelbarrow to allow collection of images of mature maize root systems at an average rate of up to 65.3 tubes per day per camera. This resulted in over 300,000 images being collected in as little as 11 days for a single experiment.

CONCLUSION

The scale of minirhizotron installation was increased by two orders of magnitude by simultaneously using four tractor-mounted, hydraulic soil corers with modifications to ensure high quality, rapid operation. Image collection can be achieved at the corresponding scale using commercially available minirhizotron camera systems. Along with recent advances in image analysis, these advances will allow use of minirhizotrons at unprecedented scale to address key knowledge gaps regarding genetic and environmental effects on root system size and distribution in the field.

Improving drought tolerance in rice: Ensuring food security through multi-dimensional approaches

Drought has been highly prevalent around the world especially in Sub-Saharan Africa and South-East Asian countries. Consistent climatic instabilities and unpredictable rainfall patterns are further worsening the situation. Rice is a C₃ staple cereal and an important food crop for the majority of the world's population and drought stress is one of the major growth retarding threats for rice that slashes down grain quality and yield. Drought deteriorates rice productivity and induces various acclimation responses that aids in stress mitigation. However, the complexity of traits associated with drought tolerance has made the understanding of drought stress-induced responses in rice a challenging process. An integrative understanding based on physiological adaptations, omics, transgenic and molecular breeding approaches successively backed up to developing drought stress-tolerant rice. The review represents a step forward to develop drought-resilient rice plants by exploiting the knowledge that collaborates with omics-based developments with integrative efforts to ensure the compilation of all the possible strategies undertaken to develop drought stress-tolerant rice.

Comparative Analysis of Chitin SynthaseA dsRNA Mediated RNA Interference for Management of Crop Pests of Different Families of Lepidoptera

RNA interference (RNAi) is a sequence-specific down-regulation in the expression of a particular gene, induced by double-stranded RNA (dsRNA). Feeding of dsRNA either directly or through transgenic plants expressing dsRNA of insect genes has been proven successful against lepidopteran and coleopteran pests, establishing an additional alternative to control insect pests. Lepidopteran crop pests including Spodoptera litura (Fabricius) (Noctuidae), Chilo partellus (Swinhoe) (Crambidae), Plutella xylostella (Linnaeus) (Plutellidae), and Maruca vitrata (Fabricius) (Pyralidae) are the devastating pests of a variety of crops. To tap the potential of RNAi against insect pests, a gene coding for the key enzyme in chitin biosynthesis in arthropods, the chitin synthaseA (CHSA), has been targeted through an exogenous delivery of dsRNA and plant-mediated RNAi. The introduction of dsCHSA caused "Half ecdysis" and "Black body" type lethal phenotypes and a significant reduction in larval body weight. Subsequent RT-qPCR analysis demonstrated the down-regulation of CHSA gene transcripts from 1.38- to 8.33-fold in the four target species. Meanwhile, when S. litura larvae fed with leaves of transgenic tobacco plants expressing dsSlCHSA, the mRNA abundance of CHSA gene was significantly decreased resulting in lethal phenotypes like "Double head formation," "Half ecdysis," and "Black body." In addition, abnormalities in pupal-adult and adult stage were also documented, strongly suggesting the RNAi effect of CHSA gene at late developmental stages. Overall, the results demonstrated that CHSA gene expression in Lepidopteran crop pests could be suppressed by application of dsRNA either as feeding or through transgenic crop plants.